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Biases in the Maximum Simulated Likelihood Estimation of the Mixed Logit Model

Author

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  • Maksat Jumamyradov

    (Department of Economics, University of South Florida, Tampa, FL 33620, USA)

  • Murat Munkin

    (Department of Economics, University of South Florida, Tampa, FL 33620, USA)

  • William H. Greene

    (Department of Economics, University of South Florida, Tampa, FL 33620, USA)

  • Benjamin M. Craig

    (Department of Economics, University of South Florida, Tampa, FL 33620, USA)

Abstract

In a recent study, it was demonstrated that the maximum simulated likelihood (MSL) estimator produces significant biases when applied to the bivariate normal and bivariate Poisson-lognormal models. The study’s conclusion suggests that similar biases could be present in other models generated by correlated bivariate normal structures, which include several commonly used specifications of the mixed logit (MIXL) models. This paper conducts a simulation study analyzing the MSL estimation of the error components (EC) MIXL. We find that the MSL estimator produces significant biases in the estimated parameters. The problem becomes worse when the true value of the variance parameter is small and the correlation parameter is large in magnitude. In some cases, the biases in the estimated marginal effects are as large as 12% of the true values. These biases are largely invariant to increases in the number of Halton draws.

Suggested Citation

  • Maksat Jumamyradov & Murat Munkin & William H. Greene & Benjamin M. Craig, 2024. "Biases in the Maximum Simulated Likelihood Estimation of the Mixed Logit Model," Econometrics, MDPI, vol. 12(2), pages 1-15, March.
    • Handle: RePEc:gam:jecnmx:v:12:y:2024:i:2:p:8-:d:1365210
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    References listed on IDEAS

    as
    1. William Greene & David Hensher, 2010. "Does scale heterogeneity across individuals matter? An empirical assessment of alternative logit models," Transportation, Springer, vol. 37(3), pages 413-428, May.
    2. Ossama Elshiewy & German Zenetti & Yasemin Boztug, 2017. "Differences Between Classical and Bayesian Estimates for Mixed Logit Models: A Replication Study," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(2), pages 470-476, March.
    3. Marco A. Palma & Dmitry V. Vedenov & David Bessler, 2020. "The order of variables, simulation noise, and accuracy of mixed logit estimates," Empirical Economics, Springer, vol. 58(5), pages 2049-2083, May.
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